Batch Simulation Run

This RMarkdown file executes run type of batch simulation. The script utilizes the gastroPlusAPI package to communicate with the GastroPlus X 10.2 service.A batch simulation allows you to run multiple defined simulations for any number of compounds or scenarios.

The output from the batch simulation run is then used to create a Cp-time plot.

To customize your study with this script for a different simulation / project / variables, please make changes to the set-input-information code-chunk.

Configure required packages

Load other necessary packages(tidyverse) required to execute the data manipulation and visualization in the script
Load gastroPlusAPI package

R

library(tidyverse)
library(gastroPlusAPI)

Set working directory

Set working directory as the current source editor context

R

if (rstudioapi::isAvailable()){
  current_working_directory <- dirname(rstudioapi::getSourceEditorContext()$path)
  setwd(current_working_directory)
}

Start GPX Service

Establishes a connection to the GastroPlus service, which allows R to communicate with GastroPlus through its API

R

gpx_service <- start_service(verbose=FALSE)

CODE

✔ Configured the GastroPlus Service

R

gpx_service$is_alive()

CODE

[1] TRUE

Setup Input Information

Make modification to the variables in this chunk to customize your workflow.

project_path: Location of the gpx project

simulation_name: Simulation name that will be included to the run

run_name: Name of the run that will be created

observed_data_group_names & observed_data_series_names: Observed data details to be used for plotting. Details can be retrieved by get_exposure_data().

R

project_path <- "../../ProjectFiles/GPX Library.gpproject"

simulation_name <- "Metoprolol Tartrate 200mg PO tablet"

run_name <- "BatchSimRun"

compound_name <- "Metoprolol Tartrate"

observed_data_group_name <- "Metoprolol Tartrate PO Cp time"
observed_data_series_name <- "Metoprolol Tartrate Exposure Plasma"

Execute the Run

Based on the details mentioned in the previous code chunk, the batch simulation run will be created, executed and results will be shown

R

#Load the project
open_project(project_path)

#Create run and execute simulation
create_run(run_name = run_name, run_type = RunType$BatchSimulation)

add_simulations_to_run(run_name = run_name, simulation_names = simulation_name)

execute_run(run_name = run_name)

Process Run Output

This section retrieves and processes the results from the batch simulation run, including:

Getting summary output with pharmacokinetic parameters

Identifying and retrieving the concentration-time series data

Retrieving observed data for comparison

R

#Get simulation keys and summary output

simulation_keys <- get_simulation_keys(run_name)
summary_output <- get_summary_output(run_name, simulation_keys$simulation_iteration_key_name)

#Get all available series outputs
available_conc_series <- get_available_series_output(run_name, OutputSeriesType$Concentration, simulation_name)

# Check whether we need to use compartmental or PBPK concentration data
# First try to find plasma concentration in systemic circulation (compartmental model)
plasma_concentration_series_descriptor <- paste(
  compound_name,
  CompartmentType$SystemicCirculation,
  StateType$ConcentrationPlasma,
  sep = " - "
)
# Alternative: check for venous return concentration (PBPK model)
venous_return_concentration_series_descriptor <- paste(
  compound_name,
  CompartmentType$VenousReturn,
  StateType$ConcentrationPlasma,
  sep = " - "
)

# Select the appropriate concentration-time descriptor based on availability
CpTime_descriptor <- ""

if(plasma_concentration_series_descriptor %in% available_conc_series$state_descriptor){
  CpTime_descriptor <- plasma_concentration_series_descriptor
}else if(venous_return_concentration_series_descriptor %in% available_conc_series$state_descriptor) {
   CpTime_descriptor <- venous_return_concentration_series_descriptor
}

# Get the concentration-time series data
series_data <- get_series_data(run_name, simulation_name, CpTime_descriptor)

#Get observed data of simulation
observed_series_key <- SeriesKey$new(group_type = ObservedDataGroupType$ExposureData, 
              group_name = observed_data_group_name, 
              series_type = ObservedDataSeriesType$UncertainConcentrationSeries, 
              series_name = observed_data_series_name)

# Get the observed data series
observed_series <- get_observed_series(observed_series_key)$series_as_data_frame()

Plot Results

Creates a concentration-time plot comparing the simulated results with observed data. This visualization helps assess the predictive performance of the simulation.

R

#Plotting
ggplot() + 
  geom_line(data=series_data, 
            mapping=aes(x=independent, y=dependent),
            linewidth=1.25, 
            color="#051082") +
  geom_point(data=observed_series, 
             mapping=aes(x=independent, y=dependent),
             size = 3.5, 
             alpha=0.75, 
             shape=22, 
             color="#051082") +
  theme_classic() + 
  labs(title= paste0(simulation_name," Cp-Time Graph"), x= paste("Time (", series_data$independent_unit[[1]], ")"), y=paste("Concentration (", series_data$dependent_unit[[1]], ")")) +
  theme(
    axis.title.x = element_text(face = "bold", size = 12),
    axis.title.y = element_text(face = "bold", size = 12),
    axis.text.x = element_text(face = "bold", size = 11),
    axis.text.y = element_text(face = "bold", size = 11),
    plot.title = element_text(hjust = 0.5, face = "bold", size = 12, color="#051082")
  )

Kill GPX Service

R

gpx_service$kill()

CODE

[1] TRUE